Reported cases as a percentage of total population

cases_deaths_world <- covnat_weekly
df1 <- cases_deaths_world %>%
  filter(date == max(date), !is.na(pop)) %>% 
  mutate(pop_percentage_cases = round((cu_cases/pop)*100, digits = 2),
         mytext = paste("Country: ", cname, "\n", 
                        "Percentage: ", pop_percentage_cases, "%", sep=""))
  
plot_ly(df1, type='choropleth', 
        locations=df1$iso3, 
        z=df1$pop_percentage_cases,
        text = df1$mytext,
        colorscale= "#de2d26") %>%
        layout(title = "Percentage of Reported Covid-19 Cases")

Interpretation

Statewise analysis of reported Covid-19 cases in USA

df4 <- nytcovcounty  

df4 %>%
  mutate(ym = format(date, '%Y-%m')) %>% 
  group_by(ym, state) %>% 
  summarize(ym_sum = sum(cases)) %>%
   
  ggplot(aes(x = ym, fill = ym_sum, y = reorder(state,ym_sum))) + 
   geom_raster()+ 
   scale_fill_viridis_c() +
   ggtitle("Reported Covid-19 cases by State in USA") +
   labs(x = "Month", y = "State Name")

Interpretation

Analysis of Covid-19 deaths by age in USA

df2 <- nchs_sas 

age_group_names <- c("Under 1 year","1-4 years","5-14 years","15-24 years",
                     "25-34 years","35-44 years","45-54 years","55-64 years",
                     "65-74 years","75-84 years","85 years and over")
 
selected_data <- df2 %>% 
  filter(group == "By Month",
         sex == "All Sexes",
         age_group %in% age_group_names,
         state != "United States")  # Remove data stored 
                                    # as United States (repeated values) 

# Assign zero to missing values in covid-19 deaths
selected_data$covid_19_deaths[is.na(selected_data$covid_19_deaths)] <- 0

selected_data %>%
  filter(covid_19_deaths > 0) %>%
  mutate(age_group = as.factor(age_group)) %>%
  ggplot(aes(x = covid_19_deaths,
             y = age_group, 
             fill = age_group)) + 
   geom_density_ridges(position = "raincloud", 
                      jittered_points = TRUE) +
   coord_cartesian(xlim = c(0, 300)) +
   labs(title = "Distribution of Monthly Covid-19 Deaths by Age in USA",
       x = "Number of Covid-19 Deaths",
       y = "Age Group") +
   theme_bw()

Interpretation

Analysis of reported Covid-19 deaths, Pneumonia deaths and Influenza deaths in USA

selected_data_2 <- df2 %>% 
  filter(group == "By Month",
         sex %in% c("Female", "Male"),
         age_group %in% age_group_names,
         state != "United States")  # Remove data stored 
                                    # under United States (repeated values) 

# Assign zero to missing values 
selected_data_2$covid_19_deaths[is.na(selected_data_2$covid_19_deaths)] <- 0
selected_data_2$pneumonia_deaths[is.na(selected_data_2$pneumonia_deaths)] <- 0
selected_data_2$influenza_deaths[is.na(selected_data_2$influenza_deaths)] <- 0

selected_data_2 %>%
  filter(covid_19_deaths > 0, pneumonia_deaths > 0, influenza_deaths > 0) %>%
  mutate(age_group = as.factor(age_group),
         sex = as.factor(sex)) %>%
  ggpairs(mapping = aes(color=sex, alpha =0.2),
         columns =c ("covid_19_deaths", "pneumonia_deaths", "influenza_deaths"))+
   ggtitle("Scatter plot matrix for Covid-19, Pneumonia and Influenza Deaths")

Interpretation